Image registration is an important challenge in medical image processing. The main goal in medical image registration is to calculate a geometrical transformation that aligns either the same image or different images of the same object or structure. The different images can have the same modality or different modalities. Common modalities for medical image registration include, but are not limited to: Magnetic Resonance Imaging (MRI), Computerized Tomography (CT), and Ultrasound (US).
An important subset of image registration problems deals with matching images from different image modalities sometimes referred to as multi-modality image fusion. Multi-modal image registration is particularly challenging as the relationship between grey values of multi-modal images is not always easy to determine, and in some cases (e.g. MRI to US), a functional dependency is generally missing or very difficult to identify.
One example of multi-modal image registration is the fusion of MRI images with US images in image-guided procedures, such as prostate biopsies or brachytherapy. The MRI modality provides high resolution anatomical images; however, MRI is expensive for intra-operative procedures such as prostate biopsies. On the other hand, the US modality is ideal for real-time imaging required for image guided procedures, such as prostate biopsy, but has quite poor image resolution. Fusion of these two modalities combines the advantage of real-time imaging (US) with high resolution imaging (MRI). For example, during a targeted prostate biopsy procedure, fusion of pre-operative MRI images with real-time US imaging is crucial in locating cancerous areas in ultrasound images that can be easily identified in MRI images. It would be advantageous to develop automatic image registration techniques to fuse pre-operative MRI images of the prostate with real-time trans-rectal ultrasound (TRUS) imaging.
The lack of a functional dependency between the MRI and US image modalities has made it very difficult to take advantage of intensity-based metrics for image registration. Therefore, most proposed methods of MRI-to-US image fusion are focused on point matching techniques in one of two ways: (1) a set of common landmarks (such as the contour of the urethra) that are visible in the images from both modalities are either manually or automatically extracted and used for point-based registration; or (2) the surface of the prostate is segmented within each of the two modalities using automatic or manual techniques, and the extracted cloud of points are fed to a point-based registration framework that tries to minimize the distance between the two point sets.
In the Philips Uronav system, for example, a point-based rigid registration approach is implemented to register MRI with TRUS using segmented prostate surface point data. The prostate gland is automatically segmented as a set of surface contour points in both US and MRI images. The rigid registration tries to find the best set of translation and rotation parameters that minimize the distance between the two point sets. However, the prostate is not a rigid shape, and the shape of the prostate may deform differently during acquisition of images by each of these modalities. MRI images are typically acquired while an Endorectal coil (ERC) is inserted in the rectum for enhanced image quality. The TRUS imaging is performed freehand with a TRUS probe placed in direct contact with the rectum wall adjacent to the prostate gland, thereby deforming the shape of the prostate gland during image acquisition.